基于反馈图卷积神经网络的事件检测方法

Event Detection Method Based on Feedback Graph Convolutional Networks

事件检测是自然语言处理领域的重要任务之一,其结果可以有效支撑信息抽取、文本分类和事件推理等下游任务.预训练语言模型BERT在事件检测任务上取得了显著的成绩,然而该类方法无法有效获取长距离和结构化的文本信息.为了缓解该问题,本文提出基于反馈网络的图卷积神经网络模型进行文本结构信息捕获,同时这种新方法能够有效解决图卷积神经网络带来的语义信息衰减性问题.本文首先使用BERT预训练模型获取文本的语义特征,然后使用融入反馈网络的图卷积神经网络提取文本的句法结构特征,最终使用多分类器实现对事件触发词的识别和分类.公开数据集ACE 2005上的实验结果表明,本文提出的事件检测方法在事件触发词识别和分类任务上的F1值分别达到了74.46%和79.49%,较现有工作平均提高了4.13%和4.79%.

Event detection is one of the most important tasks in the field of natural language processing(NLP).Its result is the key information supporting downstream tasks,such as information extraction,text classification and event reasoning.BERT model has achieved remarkable achievements in event detection.However,it cannot effectively obtain long-distance and structured text information.To alleviate this problem,feedback-based GCNs network is proposed to capture text structure information in this paper,and it can solve the problem of semantic information attenuation caused by GCNs.This paper first uses BERT model to obtain semantic features of the text,then adopts GCNs integrated into the feedback network to extract the syntactic structure features of the text,and finally employs multiple classifiers to identify and classify event trigger words.The experimental results on the open dataset ACE 2005 show that the F1 value of the event detection method proposed in the task of event trigger wordrecognition and classification has reached 74.46% and 79.49%, respectively, which gains an average increase of4.13% and 4.79% compared with the existing work.